1. Field of the Invention
The present invention relates to an automatic tracing algorithm for a quantitative analysis of continuous structures, and more particularly to an algorithm of a quantitative analysis that encodes voxels of images of a continuous structure and automatically traces this continuous structure.
2. Description of the Related Art
In recent years, computer software and digital image technology advance rapidly. For example, a magnetic resonance imaging (MRI) result of a brain can be divided to create independent 3D images of each tissue for an analysis according to the properties of the tissue, or a blood vessel photography is used for taking the photos of blood vessels by a confocal imaging microscope (OCT) to produce 2D or 3D images for the analysis, or the confocal laser scanning microscope or any other imaging technique is used for obtaining a complicated 3D digital image of a neural structure, a distribution of blood vessels, and so on. However, it is a very challenging problem to analyze such a large volume of 2D or 3D digital images to obtain useful and quantitatively accurate information.
U.S. Pat. No. 5,872,861 disclosed a blood vessel imaging method that compares the intensity of a pixel with a pixel at the center line of the blood vessel to find stenoses of vessel. U.S. Pat. No. 7,480,400 disclosed a neural fiber tractography for analyzing 3D digital images by a magnetic resonance imaging water molecule diffusion tensor imaging (DTI) method. The method mainly uses a group of selected three-dimensional voxels to find a seed point by a different method, and then uses a distance threshold of the seed point to eliminate the voxel so as to trace a pathway of neural fiber. In the method of finding the seed point in accordance with this patented technology, the direction of a diffusion tensor axis (or the direction of a neural fiber) of a near pixel or the included angle between the relative position of the pixel and the axis, and the fractional anisotropy are used for determining the seed point.
The neural fiber, blood vessel, collagen in skin tissue, or many polymeric materials are continuous structures, such as line-structures, tree-like structures, network-like structures or neuron-like structures. If an image of the continuous structure is analyzed by a quantitative analysis, the moving direction of the structure is traced. For example, a method for tracing the moving direction of a blood vessel is disclosed by U.S. Pat. Publication No. 2006/0056694, wherein a definite starting point is set for a 3D image of a tree-like structured anatomical coronary vessel, and then a tracing codelet (segment) is moved along a definite image of the blood vessel section by section, and information of a distance direction is created. However, the determination of the moving direction by this method generally requires a manual determination, particularly at a branch or a loop. In addition, this method can analyze a main path only. For an analysis of the branch of a tree-like structure, it is necessary to select the starting point repeatedly and use the tracing codelet analysis so that the analysis not only takes much manpower, but also lacks objectivity, affects the result and fails to achieve the effect of an automatic analysis due to human factors.
In an article of Journal of Neuroscience Methods, 178, P.197-204 in 2009 by Zlatko V. and Armen S. and U.S. Pat. No. 7,072,515, a fully automatic line-structured 2D or 3D fluorescence confocal microscopy image tracing method is disclosed. The method mainly selects a seed point in a line-structure as a starting point, and traces the center line of the line-structure from the starting point to the next position, and the next position is set as a new starting point to repeat the step of tracing the center line of the line-structure until an end point of the line-structure is reached. In FIG. 1, N×M grids are used for representing line-structured neural fibers, wherein the center line of the line-structure is traced on an X-Y plane and in a Z-axis direction, and four templates are shared, and two 2D templates are used for the left and right directions on the X-Y plane, and two 2D templates are used in the up and down directions along the Z-axis direction, and the line-structure is divided into N2 directions. For example, if N=32, then four templates are divided into 4×322=4096 directions such that the starting point Pi is moved to the position of the next starting point Pi+1 according to the axis of the line-structure so as to trace the whole line-structure. Although this method can automatically trace the whole line-structure, yet it is difficult to use the method to trace a more complicated structure, such as a branch point or a loop of the neural fiber. In addition, these seed points are generated according to a certain selected condition. If some conditions are not defined appropriately, the seed point will be insufficient for tracing the whole structure such that the structure will form unconnected segments. Now, it is necessary to connect the segments with manual intervention.
In an article of Journal of Neuroscience Methods, 184, P.169-175 by Alfredo R., Douglas B. E., Patrick R. H. and Susan L. W. in 2009, a dynamic data segment is used for forming a funnel-shaped three-dimensional voxel, and computing a moving distance by a displacement during a tracing in order to trace the whole confocal microscopy image. Although this method can trace a confocal microscopy image having a branch image, yet the method involves a large volume of computations, which is unfavorable to a quick computation.
As to a process of encoding three-dimensional voxels to a branched skeleton of a confocal microscopy image disclosed in an article of Computers in Biology and Medicine, 35, P.791-813 in 2005 by Hamid S. Z., Ali S., Mohammad-Mehdi K., Zheng G. Z., Reza A. Z., Mahnaz M. and Michael C., the confocal microscopy image is rebuilt by a quantitative method. This method can connect confocal microscopy images with disconnected branches into a connected image, or trim an image branch, and apply the coded result to calculate the length. However, this method still requires manual intervention.
In view of the insufficiency of the prior art, an automatic tracing algorithm for a continuous structure without manual intervention is introduced, and the tracing algorithm with the least computing volume is urgently required for automatically tracing and analyzing image data of the continuous structure including the neural fiber, blood vessel, collagen in skin tissue of a line-structure, a tree-like structure, a network-like structure or a neuron-like structure, or images of various types of polymeric material fibers.